



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :		Tuple		      =    logging.get_logger(__name__)

_lowerCamelCase    :		str		      =    {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 						lowercase    (							_A):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		'gpt_neox'







						def __init__(  self    :				List[str]       ,	snake_case    :				Union[str, Any]=50432       ,	snake_case    :				Dict=6144       ,	snake_case    :				str=44       ,	snake_case    :				Any=64       ,	snake_case    :				List[Any]=24576       ,	snake_case    :				str="gelu"       ,	snake_case    :				Union[str, Any]=0.25       ,	snake_case    :				int=10000       ,	snake_case    :				Optional[int]=0.0       ,	snake_case    :				str=0.0       ,	snake_case    :				str=0.1       ,	snake_case    :				Union[str, Any]=2048       ,	snake_case    :				Optional[Any]=0.02       ,	snake_case    :				Optional[int]=1E-5       ,	snake_case    :				Optional[Any]=True       ,	snake_case    :				str=0       ,	snake_case    :				Any=2       ,	snake_case    :				Union[str, Any]=False       ,	snake_case    :				List[Any]=True       ,	snake_case    :				Union[str, Any]=None       ,	**snake_case    :				List[Any]       ,	):



											'''simple docstring'''


											super().__init__(bos_token_id=UpperCamelCase__       ,	eos_token_id=UpperCamelCase__       ,	**UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     List[str]						=				max_position_embeddings
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				hidden_size
											SCREAMING_SNAKE_CASE							:     Tuple						=				num_hidden_layers
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				num_attention_heads
											SCREAMING_SNAKE_CASE							:     List[Any]						=				intermediate_size
											SCREAMING_SNAKE_CASE							:     Dict						=				hidden_act
											SCREAMING_SNAKE_CASE							:     str						=				rotary_pct
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				rotary_emb_base
											SCREAMING_SNAKE_CASE							:     Any						=				attention_dropout
											SCREAMING_SNAKE_CASE							:     List[str]						=				hidden_dropout
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				classifier_dropout
											SCREAMING_SNAKE_CASE							:     Dict						=				initializer_range
											SCREAMING_SNAKE_CASE							:     int						=				layer_norm_eps
											SCREAMING_SNAKE_CASE							:     str						=				use_cache
											SCREAMING_SNAKE_CASE							:     Any						=				tie_word_embeddings
											SCREAMING_SNAKE_CASE							:     List[str]						=				use_parallel_residual
											SCREAMING_SNAKE_CASE							:     Tuple						=				rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																raise ValueError(
																    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											if self.rope_scaling is None:
																return

											if not isinstance(self.rope_scaling       ,	UpperCamelCase__	) or len(self.rope_scaling	) != 2:
																raise ValueError(
																    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
																    f'''got {self.rope_scaling}'''	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.rope_scaling.get('type'       ,	UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.rope_scaling.get('factor'       ,	UpperCamelCase__	)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																raise ValueError(
																    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''	)
											if rope_scaling_factor is None or not isinstance(UpperCamelCase__       ,	UpperCamelCase__	) or rope_scaling_factor <= 1.0:
																raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''	)

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					int     ,				*UpperCamelCase__     :					Optional[Any]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      None:







    """simple docstring"""






    warnings.warn(
        """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
        """ use BeitImageProcessor instead."""     ,				UpperCamelCase__     ,				)
    super().__init__(*UpperCamelCase__     ,				**UpperCamelCase__       )

